How Does Vibe Coding Work? The Technology Explained
Demystifying the AI revolution in software development - from transformers to token prediction
Founder, Natively
How does vibe coding work is the question on every developer and entrepreneur's mind in 2026. Since Andrej Karpathy coined the term in February 2025, vibe coding has transformed from a quirky concept into the Collins Dictionary Word of the Year for 2025. But what exactly happens when you type a description and AI generates working code? Let's demystify the technology behind this revolution.
Key Takeaways
- LLMs use transformer architecture with self-attention mechanisms to understand context and generate code
- Vibe coding was coined by Andrej Karpathy in February 2025 and has seen 6,700% search growth
- About 45% of AI-generated code has security flaws - human review remains essential
- 51% of developers now use AI coding tools daily, generating 41% of all code
- The feedback loop is key - iterating with AI through conversation refines output
Vibe Coding by the Numbers
Sources: MIT Technology Review, GitHub
What is Vibe Coding? The Magic Demystified
Vibe coding is an AI-assisted software development practice where you describe what you want in natural language, and AI generates working code. The term was introduced by Andrej Karpathy, the influential AI researcher and former Tesla AI director, in a viral post on X (Twitter) on February 2, 2025.
"There's a new kind of coding I call 'vibe coding', where you fully give in to the vibes, embrace exponentials, and forget that the code even exists."
— Andrej Karpathy, February 2, 2025
The key distinction from regular AI-assisted coding? In vibe coding, you accept AI-generated code without fully understanding it. As programmer Simon Willison clarified: "If an LLM wrote every line of your code, but you've reviewed, tested, and understood it all, that's not vibe coding - that's using an LLM as a typing assistant."
How Vibe Coding Works: The Pipeline
You describe what you want in plain English - no code syntax required
The AI analyzes your intent using attention mechanisms
Token-by-token, the model generates syntactically correct code
You receive working code and can refine with follow-up prompts
Click or hover over each step to learn more
Large Language Models (LLMs) Explained Simply
At the heart of vibe coding are Large Language Models - neural networks trained on vast amounts of text and code. Think of them as incredibly sophisticated pattern-matching systems that have learned the "grammar" of both human language and programming languages.
The Scale of Modern LLMs
- •GPT-4: Estimated 1.7 trillion parameters
- •Claude 3: Specialized for reasoning and code
- •DeepSeek-R1: 671B parameters, open-weight
- •Code Llama: Specialized for programming tasks
What They Learned From
- ✓Billions of lines of open-source code
- ✓Documentation and Stack Overflow answers
- ✓GitHub repositories and pull requests
- ✓Technical books and tutorials
These models don't "understand" code the way humans do. Instead, they predict the most likely next token (word or code fragment) based on patterns seen during training. When you ask for a function that sorts a list, the model has seen thousands of similar functions and generates what statistically makes sense.
How AI Understands Your Descriptions
When you type "create a login form with email and password validation," the AI doesn't read it like you do. Here's what actually happens behind the scenes:
1Tokenization
Your text is broken into tokens - roughly word-sized pieces. "create a login form" becomes ["create", "a", "login", "form"]. Code has its own tokens: function names, operators, brackets.
2Embedding
Each token is converted to a high-dimensional vector (list of numbers). Similar concepts end up with similar vectors - "login" and "authentication" are mathematically close.
3Self-Attention
The attention mechanism weighs how each word relates to every other word. "Validation" gets connected to "email" and "password," not to "form."
4Context Building
Multiple attention layers (often 32-96 in modern models) progressively build understanding. Early layers capture syntax; later layers understand intent and context.
The Code Generation Process
Once the AI understands your request, it generates code token by token. This is where the "magic" happens - but it's really just sophisticated pattern prediction.
| Step | What Happens | Example |
|---|---|---|
| Prompt Analysis | Identify key requirements and patterns | "login form" → Form component needed |
| Structure Selection | Choose appropriate code architecture | React component with useState |
| Token Generation | Generate code one token at a time | function → LoginForm → ( → ) → { |
| Coherence Check | Each new token must fit context | After "const" comes variable name |
| Completion | Continue until logical end point | export default LoginForm; |
The model's "temperature" setting affects creativity. Lower temperatures produce more predictable, conventional code. Higher temperatures can generate novel solutions but risk introducing errors. Most coding tools use moderate temperatures to balance reliability with flexibility.
How AI Handles Context and Iteration
One of vibe coding's most powerful features is iterative refinement. You don't need to get your prompt perfect the first time - you can have a conversation with the AI.
Context Window: The AI's Working Memory
Context window size determines how much code and conversation history the AI can consider at once.
Modern vibe coding tools like Cursor and specialized platforms maintain conversation context, letting you say things like "now add dark mode support" without re-explaining your entire app. The AI remembers what you've built together.
Training Data and Code Knowledge
Why can AI write working code? Because it has seen an enormous amount of human-written code. The training process exposes models to:
Public Repositories
Millions of GitHub projects across every programming language and framework
Q&A Sites
Stack Overflow answers, documentation, and community discussions
Code Reviews
Pull requests with human feedback on what makes code better
According to recent research from arXiv, Python dominates as the preferred language in LLM code generation, followed by JavaScript and TypeScript. This reflects the training data distribution - languages with more public code see better generation quality.
The Feedback Loop: How You Guide the AI
Vibe coding isn't passive - your feedback shapes the output. Karpathy described his workflow: "When I got error messages I'd just copy paste them in with no comment, and usually that would fix it."
Effective Feedback Patterns
- ✓Share error messages directly with the AI
- ✓Describe visual issues: "button is too small"
- ✓Reference specific elements to modify
- ✓Provide context from your domain
Common Pitfalls to Avoid
- ✗Vague requests: "make it better"
- ✗Conflicting requirements in one prompt
- ✗Ignoring security considerations
- ✗Skipping testing of generated code
Platforms like Natively streamline this feedback loop by providing real-time previews. You can see your mobile app take shape as you describe features, making the iteration process visual and intuitive - no need to decode terminal output.
Test Your Knowledge
Question 1 of 4Who coined the term "vibe coding" and when?
Technical Limitations and Why They Exist
Vibe coding isn't without risks. Understanding the limitations helps you use it responsibly.
| Limitation | Why It Exists | Mitigation |
|---|---|---|
| Security Vulnerabilities | Training data includes vulnerable code patterns | Use security scanning, human review |
| Hallucinations | Model predicts plausible but incorrect code | Test thoroughly, verify APIs exist |
| Context Loss | Limited context window size | Keep related code in same session |
| Business Logic Gaps | AI lacks domain-specific knowledge | Provide detailed requirements |
| Package Hallucinations | AI invents non-existent libraries | Verify packages before installing |
⚠️ Security Warning
According to Kaspersky research, 45% of AI-generated code contains security flaws. A December 2025 assessment found 69 vulnerabilities across code from five major vibe coding tools. For production applications, always include human security review.
Ready to Try Vibe Coding?
Experience the power of AI code generation with Natively. Describe your mobile app idea in plain English and watch it come to life - with the safety of a production-ready framework.
Frequently Asked Questions
What is vibe coding?
Vibe coding is an AI-assisted software development practice where developers describe what they want in natural language, and AI tools generate working code. Coined by Andrej Karpathy in February 2025, it emphasizes accepting AI-generated code without fully reviewing it, focusing on outcomes rather than code inspection.
How do LLMs generate code from natural language?
LLMs use transformer architecture with self-attention mechanisms to process your natural language input. The text is tokenized, processed through multiple attention layers that understand context and relationships, then the model predicts the most likely code tokens based on patterns learned from billions of lines of code during training.
Is vibe coding safe to use for production applications?
Vibe coding carries risks for production use. Research shows about 45% of AI-generated code contains security vulnerabilities. While excellent for prototyping and MVPs, production code should undergo human review, testing, and security analysis. Platforms like Natively help mitigate these risks with built-in best practices.
What tools are used for vibe coding in 2026?
Popular vibe coding tools include Cursor (18% market share), GitHub Copilot (42% market share), Replit Agent, and specialized platforms like Natively for mobile apps. These tools leverage advanced LLMs like GPT-4, Claude, and specialized code models to generate production-ready code.
How accurate is AI-generated code?
AI code accuracy varies by task complexity. For common patterns and boilerplate, accuracy exceeds 90%. However, for complex business logic or security-sensitive code, human oversight remains essential. Studies show AI generates approximately 41% of all code written today, but quality depends heavily on clear prompts and proper context.